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A diverse set of tectonic features and the recent availability of high-quality broadband seismic data from the USArray and other stations on the northern Great Plains of North America provide a distinct opportunity to test different anisotropy-forming mechanisms. A total of 4138 pairs of well-defined splitting parameters observed at 445 stations show systematic spatial variations of anisotropic characteristics. Azimuthally invariant fast orientations subparallel to the absolute plate motion (APM) direction are observed at most of the stations on the Superior Craton and the southern Yavapai province, indicating that a single layer of anisotropy with a horizontal axis of symmetry is sufficient to explain the anisotropic structure. For areas with simple anisotropy, the application of a procedure for estimating the depth of anisotropy using spatial coherency of splitting parameters results in a depth of 200-250 km, suggesting that the observed anisotropy mostly resides in the upper asthenosphere. In the vicinity of the northern boundary of the Yavapai province and the Wyoming Craton, the splitting parameters can be adequately explained by a two-horizontal layer model. The lower layer has an APM-parallel fast orientation, and the upper layer has a fast orientation that is mostly consistent with the regional strike of the boundary. Based on the splitting measurements and previous results from seismic tomography and geodynamic modeling, we propose a model involving deflecting of asthenosphere flow by the bottom of the lithosphere and channeling of flow by a zone of thinned lithosphere approximately along the northern boundary of the Yavapai province.

Without a good understanding of the faults and fractures present in a net pay zone, the possibility of wasting valuable resources is high. We characterize here fractures and faults within the Utica Shale by integrating routinely used methods, such as geometric attributes (Dip filter, Similarity, Fault enhanced similarity) and comparing them with a new fault attribute that extracts faults and fractures, and improves their visibility. The new method also helps minimize random noise in the seismic data. In order to fully optimize faults and structures, we first filtered the seismic data with a structurally oriented filter to reduce the noise and improve the imaging quality. Using a single attribute to derive information from faults and fractures is not optimum; therefore, we employed a second step, applying several conventional attributes, such as similarity, curvature, and fault enhanced filters. These successfully identified the fault and fracture geometries. A comparatively new fault attribute, known as Fault likelihood and defined as a power of semblance, was then used to capture and delineate faults and fractures in the same Utica Shale area. This attribute is created by scanning a range of fault dips to identify maximum likelihood. The value range of the fault likelihood attribute is between 0 and 1. In order to obtain even sharper fault planes, a filtering step is also performed. When compared to traditional attributes, the faults and fractures are better defined by the new method. In addition, the new fault likelihood attribute is extremely versatile and can be used to characterize fault and fracture proximity and density.

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